PROJECT SUMMARY Cognitive resilience to Alzheimer's Disease (AD) is a phenomenon whereby individuals are resistant to its most damaging effects on cognition, despite the presence of known familial AD (FAD) mutations or advanced neuropathology. Genetic factors promoting cognitive resilience may thus provide key targets for treatment and prevention of AD. Our overall objective is to identify drivers of cognitive resilience by using network approaches to integrate data collected from mouse FAD models with human AD data. To this end, we will in Aim 1 use a novel mouse panel that incorporates high-risk human FAD mutations on a segregated background of genetic diversity (BXD panel) to identify modifiers that contribute to AD resilience in a `humanized' mouse population. High-dimensional molecular, cognitive and pathologic data from these mice will be integrated to predict resilience factors and networks using causal inference analyses. In Aim 2, we will test two set of genes for association with resilience in humans with asymptomatic AD: 1) a previously validated list of genes identified by proteomics and behavioral analyses to be associated with exceptional cognitive longevity in mice and 2) novel genes and networks implicated by our analyses in Aim 1. In Aim 3, we will validate resilience factors and determine their effects on memory-relevant brain networks in powerful AD mouse models, testing both novel candidates identified in Aims 1 and 2 and a priori candidates (e.g., Trpc3, Adamts17 and Hp1bp3). This project will deliver novel, validated targets for promoting healthy brain aging and resilience to AD. Moreover, we will provide mechanistic insight into AD resilience, specifically supporting or refuting our hypothesis that modifiers of cognition in FAD similarly influence late-onset AD by preserving the functional connectivity of memory relevant networks. We will annotate, curate, and rapidly disseminate the data to the broad scientific community prior to publication via the NIA-supported AMP-AD Knowledge Portal to maximize the usability of these data for meta-analysis and systems biology research.